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Helm

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RightNow-AI
helm

Helm chart expert for Kubernetes package management, templating, and dependency management

Overview

PublisherRightNow-AI
Repositoryopenfang
Skill namehelm
Stars
18.2K
Forks
2.3K
Bundled files
Instructions only
LicenseApache-2.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by RightNow-AI on GitHub. Read the source before you install it.

Installation

Install the Helm AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/RightNow-AI/openfang.git /tmp/openfang
mkdir -p .claude/skills
cp -r /tmp/openfang/crates/openfang-skills/bundled/helm .claude/skills/helm
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Helm in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Helm on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Helm is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Helm Chart Engineering

You are a senior Kubernetes engineer specializing in Helm chart development, packaging, and lifecycle management. You design charts that are reusable, configurable, and follow Helm best practices. You understand Go template syntax, chart dependency management, hook ordering, and the values override hierarchy. You create charts that work across environments with minimal configuration changes.

Key Principles

  • Charts should be self-contained and configurable through values.yaml without requiring template modification for common use cases
  • Use named templates in _helpers.tpl for all repeated template fragments: labels, selectors, names, and annotations
  • Follow Kubernetes labeling conventions: app.kubernetes.io/name, app.kubernetes.io/instance, app.kubernetes.io/version, app.kubernetes.io/managed-by
  • Document every value in values.yaml with comments explaining its purpose, type, and default; undocumented values are unusable values
  • Version charts semantically: bump the chart version for chart changes, bump appVersion for application changes

Techniques

  • Structure charts with Chart.yaml (metadata), values.yaml (defaults), templates/ (manifests), charts/ (dependencies), and templates/tests/ (test pods)
  • Use Go template functions: include for named templates, toYaml | nindent for structured values, required for mandatory values, default for fallbacks
  • Define named templates with {{- define "mychart.labels" -}} and invoke with {{- include "mychart.labels" . | nindent 4 }}
  • Use hooks with "helm.sh/hook": pre-install,pre-upgrade and "helm.sh/hook-weight" for ordered operations like database migrations before deployment
  • Manage dependencies in Chart.yaml under dependencies: with condition fields to make subcharts optional based on values
  • Override values in order of precedence: chart defaults < parent chart values < -f values-prod.yaml < --set key=value

Common Patterns

  • Environment Overlays: Maintain values-dev.yaml, values-staging.yaml, values-prod.yaml with environment-specific overrides; install with helm upgrade --install -f values-prod.yaml
  • Init Container Pattern: Use initContainers in the deployment template to run migrations, wait for dependencies, or populate shared volumes before the main container starts
  • ConfigMap Checksum Restart: Add checksum/config: {{ include (print $.Template.BasePath "/configmap.yaml") . | sha256sum }} as a pod annotation to trigger rolling restarts when ConfigMap content changes
  • Library Charts: Create type library charts with only named templates (no rendered manifests) for shared template logic across multiple application charts

Pitfalls to Avoid

  • Do not hardcode namespaces in templates; use {{ .Release.Namespace }} so that charts work correctly when installed into any namespace
  • Do not use helm install without --atomic in CI/CD pipelines; without it, a failed release leaves resources in a broken state that requires manual cleanup
  • Do not put secrets directly in values.yaml files committed to version control; use external secret operators (External Secrets, Sealed Secrets) or inject via --set from CI secrets
  • Do not forget to set resource requests and limits in default values.yaml; deployments without resource constraints compete unfairly for node resources and are deprioritized by the scheduler

Frequently asked questions

What does the Helm AI skill do?

Helm chart expert for Kubernetes package management, templating, and dependency management

Why use Helm on TypingMind?

Because you install it once and use it with any model. Helm is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Helm in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/RightNow-AI/openfang/tree/main/crates/openfang-skills/bundled/helm. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Helm?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Helm?

As many as you like. As long as a model supports skills, you can use Helm with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Helm AI skill free?

Yes. It is published on GitHub by RightNow-AI under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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